Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.
Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.
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DOI：
10.3390/metabo3030741
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发表时间：
2013-09-03
期刊：
影响因子：
4.1
通讯作者：
Kolker E
中科院分区：
文献类型：
--
作者：
Stanberry L;Mias GI;Haynes W;Higdon R;Snyder M;Kolker E
综合个人组学分析（iPOP）是一项开创性的研究，它结合了基因组学、转录组学、蛋白质组学、代谢组学和个体在14个月期间的自身抗体分析。观察期包括两次病毒感染：一次是人鼻病毒，一次是呼吸道合胞病毒。剖面研究为生物体的生物学功能提供了一个翔实的快照。我们假设该通路的表达水平与疾病状态相关。为了验证这一假设，我们使用生物学途径整合代谢组学和蛋白质组学iPOP数据。该方法在考虑路径结构和纵向设计的同时，计算每个时间点路径的差异表达水平。由此产生的通路水平显示与疾病状态密切相关。此外，我们确定代谢物表达水平的时间模式。代谢物表达水平的变化似乎也与疾病状态一致。综合分析的结果表明，生物学途径的变化可用于预测和监测该疾病。iPOP实验设计、数据采集和分析问题在更广泛的个人分析背景下进行了讨论。
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.